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/*
* ******************************************************************************
* *
* *
* * This program and the accompanying materials are made available under the
* * terms of the Apache License, Version 2.0 which is available at
* * https://www.apache.org/licenses/LICENSE-2.0.
* *
* * See the NOTICE file distributed with this work for additional
* * information regarding copyright ownership.
* * Unless required by applicable law or agreed to in writing, software
* * distributed under the License is distributed on an "AS IS" BASIS, WITHOUT
* * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the
* * License for the specific language governing permissions and limitations
* * under the License.
* *
* * SPDX-License-Identifier: Apache-2.0
* *****************************************************************************
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package org.deeplearning4j.nn.conf.layers;
import lombok.*;
import org.deeplearning4j.nn.api.Layer;
import org.deeplearning4j.nn.api.ParamInitializer;
import org.deeplearning4j.nn.conf.InputPreProcessor;
import org.deeplearning4j.nn.conf.NeuralNetConfiguration;
import org.deeplearning4j.nn.conf.inputs.InputType;
import org.deeplearning4j.nn.conf.memory.LayerMemoryReport;
import org.deeplearning4j.nn.conf.memory.MemoryReport;
import org.deeplearning4j.nn.params.EmptyParamInitializer;
import org.deeplearning4j.optimize.api.TrainingListener;
import org.nd4j.linalg.activations.Activation;
import org.nd4j.linalg.api.buffer.DataType;
import org.nd4j.linalg.api.ndarray.INDArray;
import org.nd4j.linalg.lossfunctions.ILossFunction;
import java.util.Collection;
import java.util.Map;
@Data
@NoArgsConstructor
@ToString(callSuper = true)
@EqualsAndHashCode(callSuper = true)
public class Cnn3DLossLayer extends FeedForwardLayer {
protected ILossFunction lossFn;
protected Convolution3D.DataFormat dataFormat;
private Cnn3DLossLayer(Builder builder) {
super(builder);
this.lossFn = builder.lossFn;
this.dataFormat = builder.dataFormat;
}
@Override
public Layer instantiate(NeuralNetConfiguration conf, Collection trainingListeners,
int layerIndex, INDArray layerParamsView, boolean initializeParams, DataType networkDataType) {
org.deeplearning4j.nn.layers.convolution.Cnn3DLossLayer ret =
new org.deeplearning4j.nn.layers.convolution.Cnn3DLossLayer(conf, networkDataType);
ret.setListeners(trainingListeners);
ret.setIndex(layerIndex);
ret.setParamsViewArray(layerParamsView);
Map paramTable = initializer().init(conf, layerParamsView, initializeParams);
ret.setParamTable(paramTable);
ret.setConf(conf);
return ret;
}
@Override
public ParamInitializer initializer() {
return EmptyParamInitializer.getInstance();
}
@Override
public InputType getOutputType(int layerIndex, InputType inputType) {
if (inputType == null || (inputType.getType() != InputType.Type.CNN3D
&& inputType.getType() != InputType.Type.CNNFlat)) {
throw new IllegalStateException("Invalid input type for CnnLossLayer (layer index = " + layerIndex
+ ", layer name=\"" + getLayerName() + "\"): Expected CNN3D or CNNFlat input, got "
+ inputType);
}
return inputType;
}
@Override
public InputPreProcessor getPreProcessorForInputType(InputType inputType) {
return InputTypeUtil.getPreProcessorForInputTypeCnn3DLayers(inputType, getLayerName());
}
@Override
public LayerMemoryReport getMemoryReport(InputType inputType) {
//During inference and training: dup the input array. But, this counts as *activations* not working memory
return new LayerMemoryReport.Builder(layerName, getClass(), inputType, inputType).standardMemory(0, 0) //No params
.workingMemory(0, 0, 0, 0)
.cacheMemory(MemoryReport.CACHE_MODE_ALL_ZEROS, MemoryReport.CACHE_MODE_ALL_ZEROS) //No caching
.build();
}
@Override
public void setNIn(InputType inputType, boolean override) {
//No op
}
@Getter
@Setter
public static class Builder extends BaseOutputLayer.Builder {
/**
* Format of the input/output data. See {@link Convolution3D.DataFormat} for details
*/
protected Convolution3D.DataFormat dataFormat;
/**
* @param format Format of the input/output data. See {@link Convolution3D.DataFormat} for details
*/
public Builder(@NonNull Convolution3D.DataFormat format) {
this.setDataFormat(format);
this.setActivationFn(Activation.IDENTITY.getActivationFunction());
}
@Override
@SuppressWarnings("unchecked")
public Builder nIn(int nIn) {
throw new UnsupportedOperationException(
"Cnn3DLossLayer has no parameters, thus nIn will always equal nOut.");
}
@Override
@SuppressWarnings("unchecked")
public Builder nOut(int nOut) {
throw new UnsupportedOperationException(
"Cnn3DLossLayer has no parameters, thus nIn will always equal nOut.");
}
@Override
public void setNIn(long nIn){
throw new UnsupportedOperationException(
"Cnn3DLossLayer has no parameters, thus nIn will always equal nOut.");
}
@Override
public void setNOut(long nOut){
throw new UnsupportedOperationException(
"Cnn3DLossLayer has no parameters, thus nIn will always equal nOut.");
}
@Override
@SuppressWarnings("unchecked")
public Cnn3DLossLayer build() {
return new Cnn3DLossLayer(this);
}
}
}